Background/foreground separation is the first step in video surveillance system to detect
moving objects. Recent research on problem formulations based on decomposition into low …

Background modeling has emerged as a popular foreground detection technique for various
applications in video surveillance. Background modeling methods have become increasing …

We address the problem of compressed sensing (CS) with prior information: reconstruct a
target CS signal with the aid of a similar signal that is known beforehand, our prior …

The reconstruction of a high resolution image given a low resolution observation is an ill-
posed inverse problem in imaging. Deep learning methods rely on training data to learn an …

This paper offers a characterization of fundamental limits on the classification and
reconstruction of high-dimensional signals from low-dimensional features, in the presence of …

The development of online algorithms to track time-varying systems has drawn a lot of
attention in the last years, in particular in the framework of online convex optimization …

In linear inverse problems, the goal is to recover a target signal from undersampled,
incomplete or noisy linear measurements. Typically, the recovery relies on complex …

We propose and analyze an online algorithm for reconstructing a sequence of signals from a
limited number of linear measurements. The signals are assumed sparse, with unknown …

This paper considers online robust principal component analysis (RPCA) in time-varying
decomposition problems such as video foreground-background separation. We propose a …

We address the problem of reference-based compressed sensing: reconstruct a sparse
signal from few linear measurements using as prior information a reference signal, a signal …